{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python for Finance (2nd ed.)\n",
    "\n",
    "**Mastering Data-Driven Finance**\n",
    "\n",
    "&copy; Dr. Yves J. Hilpisch | The Python Quants GmbH\n",
    "\n",
    "<img src=\"http://hilpisch.com/images/py4fi_2nd_shadow.png\" width=\"300px\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Why Python for Finance?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zen of Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Zen of Python, by Tim Peters\n",
      "\n",
      "Beautiful is better than ugly.\n",
      "Explicit is better than implicit.\n",
      "Simple is better than complex.\n",
      "Complex is better than complicated.\n",
      "Flat is better than nested.\n",
      "Sparse is better than dense.\n",
      "Readability counts.\n",
      "Special cases aren't special enough to break the rules.\n",
      "Although practicality beats purity.\n",
      "Errors should never pass silently.\n",
      "Unless explicitly silenced.\n",
      "In the face of ambiguity, refuse the temptation to guess.\n",
      "There should be one-- and preferably only one --obvious way to do it.\n",
      "Although that way may not be obvious at first unless you're Dutch.\n",
      "Now is better than never.\n",
      "Although never is often better than *right* now.\n",
      "If the implementation is hard to explain, it's a bad idea.\n",
      "If the implementation is easy to explain, it may be a good idea.\n",
      "Namespaces are one honking great idea -- let's do more of those!\n"
     ]
    }
   ],
   "source": [
    "import this"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Python Ecosystem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300.0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 * 2.5 + 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# causes intentional error\n",
    "# log(1)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.log(1)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finance and Python Syntax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "uuid": "1447b7bb-ed26-4c0f-9e0f-222dfd5d0c9b"
   },
   "outputs": [],
   "source": [
    "S0 = 100.  \n",
    "K = 105.  # <2\n",
    "T = 1.0  \n",
    "r = 0.05  \n",
    "sigma = 0.2  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "I = 100000  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "np.random.seed(1000)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "z = np.random.standard_normal(I)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "ST = S0 * np.exp((r - sigma ** 2 / 2) * T + sigma * math.sqrt(T) * z)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "hT = np.maximum(ST - K, 0)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "uuid": "a95c7301-39f7-4d51-937a-334f051c2d9e"
   },
   "outputs": [],
   "source": [
    "C0 = math.exp(-r * T) * np.mean(hT)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "uuid": "84aab05d-40de-4ef1-b3a8-eb08f86e1662"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of the European call option: 8.019.\n"
     ]
    }
   ],
   "source": [
    "print('Value of the European call option: {:5.3f}.'.format(C0))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value of the European call option 7.989.\n"
     ]
    }
   ],
   "source": [
    "%run bsm_mcs_euro.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time-to-Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "uuid": "e16924db-8402-4bcb-a9c7-d5753346c72a"
   },
   "outputs": [],
   "source": [
    "import numpy as np  \n",
    "import pandas as pd  \n",
    "from pylab import plt, mpl  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn')  \n",
    "mpl.rcParams['font.family'] = 'serif'  \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "uuid": "22071d72-094b-4b51-aa39-cfa793d58623"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2138 entries, 2010-01-04 to 2018-06-29\n",
      "Data columns (total 1 columns):\n",
      ".SPX    2138 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 33.4 KB\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('../../source/tr_eikon_eod_data.csv',\n",
    "                  index_col=0, parse_dates=True)  \n",
    "data = pd.DataFrame(data['.SPX']) \n",
    "data.dropna(inplace=True)  \n",
    "data.info()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "uuid": "304b2f50-5b81-4e88-8fa7-ea69995cf2fe"
   },
   "outputs": [],
   "source": [
    "data['rets'] = np.log(data / data.shift(1))  \n",
    "data['vola'] = data['rets'].rolling(252).std() * np.sqrt(252)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "uuid": "b4e61939-c7fe-4a1e-a8c9-6fd755edcf86"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl0AAAFdCAYAAAA5ati2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvDW2N/gAAIABJREFUeJzs3Xdg1OXhx/H3XfbeiyTMhC97T8EBTtS6qtatddRZa9Wq1WqXtlq1VltXK/bXOurCqhURFRAFERAQCOMLBMgCssjel7vfH5ccOZKQQZLL+Lz+uu/zHfc8Ac2H5/sMi8PhQERERES6l9XTFRAREREZCBS6RERERHqAQpeIiIhID1DoEhEREekBCl0iIiIiPcDb0xVoS35+WbdPr4yICKSoqLK7v6ZXGEhtBbW3v1N7+6+B1FZQe/uTmJgQS2vn1NMFeHt7eboKPWYgtRXU3v5O7e2/BlJbQe0dKBS6RERERHqAQpeIiIhID1DoEhEREekBCl0iIiLiMUVlNWzbf9jT1egRCl0iIiLiMS9/mMbTb33PjowiT1el2yl0iYiIiMfsyi4BYN2OXA/XpPspdImIiIhHFJRUuT7nFFR4sCY9Q6FLREREPGLt9iO9W3uyS6ipq/dgbbpfr1+Rvi9YuXIFq1d/RVxcPIWFBdTW1vKrX/2W7dvTeOGF57DZbEyfPpPi4mKsVgvXXXcjf//7iyxe/CH33PMA559/Ef/4x4ts3bqZ66//CZMmTfF0k0RERLpVvd3OopV7AYgM9eNwaQ0lFbXEhgd4uGbdR6GrC/zpT4/yzjsfEhQUDMDjj/8egDFjxjF58lSqqqq44YabAbjjjp+wc+d27r//IUpKitm/fx8ANTU1PPronwgNDfVMI0RERHrQq4t3uj5PTIlmxcYcamudPV11tnoOHa7iQEEFaXsL+dGpqQQH+HTo+XlFlTz67w2cPGkQPzx5RJfWvbP6fOh6Z/ke1u/MO65neHlZqK8/ssXj9FGxXDo/pd33R0ZG8a9/vcoll1xGTEwsDzzwcIvX2Ww2iouLCQsLB+C++x7i2msvw2azMX/+aQpcIiIyINTbHazZdgiAUyYNws/HuS1Qjc0Zuv76/lbS9h5ZRiIi1J+LThre7ufX1NbzwMvfArB4TQY1tfVccfrIrqp+p2lMVxd48snnqKys4MYbr+HHP76Czz5b4nY+LW0LCxe+zF//+meuueZ6xowZB0B4eDhXX30dK1cuJzXV8ETVRUREetyB/HLX57NmDTkSuhp6upoGLoCPv9nfoedn5Ja5HX+xIRtbvb0TNe1afb6n69L5KR3qlWpJTEwI+fllbV/Yivj4eO6995fcc88DbNz4HQ899AtGjEhlxAhnvcaNm+B6vdhUZWUF6el7mDZtBs888wQPP/z7TtdBRESkL7DV2/nli98AcMb0ZGLDAwjwc8aRqhobhw5Xtnhfdl45SbHBrT7X7nBQXlVHVl45uzKLAbj6TIPXlpoA7MgoYvzwqK5sSoepp+s4HDx4AICf/exWACwWC1OnTic2Ng6bzdbm/f/85yvccMPN3HPP/WzZsoXly7/o1vqKiIh42qHDlZSU1wKwYOZgAIL8naFrg5nPg3//tsX7zKziVp+5M6OIG59YwV3PreLpt77nf9/sx2qxMGN0LJeflgrACx+k4XA4Wn1GT1Do6iS73c5Pf3ozBw8eICQklKeeepyFC1/miSceY/780zGMUezcuZ3NmzexbdtWvvxymevew4cL+ctfniI9fTcOhwMfH19SUlJ56qk/8sEHizzYKhERke51qNDZk/Wj+SmEBfsBENgQuvbklDS7vnE24xuf73KVVdfaeGvZbrLznK8pV2092Ow+u8NBkL8PU1JjAOery13HCG49oc+/XvQUq9XKe+/9D4BHH32ixWtGjRrDc8+91Kw8MjKKu+66163sj398qusrKSIi0kvs2H+YsGA/18rzCVGBrnNB/s6ZiQUl1c3uu/tHE12D4hst35jDZ+uzyCuq4s6LJ1BcXtPq90aF+bs+J0QHHVcbjpdCl4iIiHSr7Lxynnzre7eyofFHZuwH+rnHkWEJIew76BxrHRsRyOghEezIKMJWb8fby0pVjXMIz/d7Cqiz1bN9fxF+vl68ePfJvPhBGut35nH3pRNdz7v70okcLKwkNNC3u5rYLgpdIiIi0m3sdgePvLrOrSwxJojQoCMBqPH1YqMbzx3DQ/9Yy/RRsQCu2Y0Zh8r4dlsuyzZmu669+amVAMSEOV9D/uS8MZw1czDDEo6EunHDoxjn4UH0oNAlIiIi3eTL73NYtuFIQHr85lnkl1Qza0IiZaVH9l1sfL3YKCrUn1fumwcW57Gvj3MI+mOvbWj1u86ckQyAl9XqFrh6E4UuERER6XK2ejv//tR0HU8bFUt0eACxEYH4+3nTdKGmxlAFEODnhW9Dz1aj2rq219iamBJ93HXubpq9KCIiIl1uZ2aR6/PwQaHcdsE4rBZLi9daLBaSYpxrcDUd69WoorquWdmDV00lscnA+KNfUfZGvb+GIiIi0ifY7Q6qG1aV//PbmwGYMjKGa85se9eVojLnzMX4yMBm5yqrm699mRgTxO9vnElltY2auvpWA11votAlIiIix6VxBuFby/eQe9SK8ufNGeo2aL41tTbnK0S/o14tQvOerkvmjXCtYh/o790nerlAoUtERESOw4qN2bz22a4Wz91/xWQGx4W06zkpiWHsyCgiMab5Wlo3nTuGl/+3ndKKxpXsh3S+wh6k0CUiIiIdtm3fYT5bn8XWvYWuspSkMPYfLMVW72BIfAjG4Ih2P+/WC8axNb2QmWPjmp0bPTSSv/x0LrmHK6nrBRtXd5ZCl4iIiHRIXnEVT7/tvthpTLg/D1w5hdq6elZtOcgpkxM79MzgAB9mj4s/5jVxLYz36kvaDF2GYYwAHgU2AklAoWmavzMM4zfAKU0ufcw0zc8b7vkFEApEAJ+ZpvlRQ/kk4HZgHxAL3GuaZts7Q4uIiEivsXJTjuvzzy6ewMSUaBwOBxaLBX9fb06bluzB2vVe7enpigTeMk3zQwDDMLYbhrEYwDTNU46+2DCMmcA80zTPNgzDB9huGMZXQAnwOnCaaZqHDMN4GrgWWNg1TREREZHulJlbxvqdeXy6LpOYcH9+e/0M/H2dUcLSB2YPelqbocs0zfVHFVmBCgDDMB4CagAv4K+maVYC5wJrGu6tMwxjB3ASsA0IME3zUMNzVgNXodAlIiLS6730YRrrduS5js+aMdgVuKR9OvTTMgzjQmCpaZo7DcN4F9hvmmaFYRi3AX8FbsD52nBHk9tKG8rywW0B2sbyY4qICMTbu/n00a4WE9O+2RX9wUBqK6i9/Z3a238NpLaCZ9tbXFbDhp25ZB4qY+22g8ydlMhVZ40G4FBhBfsOlLgFrqEJoZxzUgpBAT6tPbJNA+3PFzoQugzDmAfMA+4CME1zW5PTy4FfNHzOA5r+JEMbylorP6aiosq2LjluMTEh5OeXtX1hPzCQ2gpqb3+n9vZfA6mt4Nn2llbWctdzq9zK3v58F29/votHrpvGP/63nYOFzt/F580ZygUnDgegsryayvLqTn1nf/7zPVaYbNc2QIZhnAOcCfwMiDcMY7ZhGE82uSQV2NPw+WNgdsN93sAY4CtgL1BlGEbj1IQ5wOL2N0NERESOh8Ph4K1lu1m4eLurbOHHR15OeVndx2X97v++cwUugMmpMd1fyX6sPbMXpwJvA98BK4Ag4HnAZhjGszh7q8bjnJWIaZprDcNYYRjGH3DOXrzbNM3ihmddBTxmGEYGznFg/+r6JomIiEhTX28+wD+X7HQrW731EEkxQWTnVwDwzB1zCAv2Y03aIf7x8Xa3awP8vPnR/BSGxA+8V4JdqT0D6TcAwR15qGmaT7ZS/j3OcV8iIiLSA9IPlDQLXI0aA9dJEwcRFuwHwOihEcSE+5Nf7Hx1mJIUxj2XTsLPt/vHV/d3mnYgIiLSB9Tb7djt4OPd9sig73cXsMHMIzYigCVrM13ll81PITEmmP2HSlm0cq+r/LRpSa7P4cF+PH7zbD5dm4kDOHNGMl7Wdo1GkjYodImIiPQBv/nnenLyK3j2zrmYmcVMMWKwHrU21rodubz04bZm9wb5e3P7heMZNcS5Lc/YYZGcPWsIS9ZmMjg2mKQY9xdaFouFBbP65v6GvZlCl4iISC9XUlFLTsOrwJ81zDScMTqWc08YSkSIH0H+PtgdjhYDF8Bvr59BZKi/W5nFYuFsBasepdAlIiLSi2UcKuO3/3f0OuWwbkeea+2swXHBzBrjXBwgwM+LP/5kNruyinnhgzSAZoFLPEOhS0REpJeqqK5zC1yzx8ax90ApuUVVbtdl5paTmetcuemsGYMJDfJ1zTRcMHNwz1VYjkmhS0REpJfamVEEQGSoH0/dNsdVvnrrQRYudq6vdflpqfzni92uc42bTceEB/D8z0/CX7MOew2FLhERkV7q04aZh7dfON6tfM74BGaOiaOmrp4gfx/yiqr4zszjrosnEuB35Fd708/iefrTEBER6UV2ZxezYmMOdTY76QdKGTs0gmEJoc2u8/ay4u3lXMrhytNHcuXpI3u6qtJBCl0iIiIeVlJeQ9q+QiJC/Fm2Idttc+lJ2nqn31DoEhER6WEfrd7HB1/vAyA6zJ+CkpY3jp6cGs28KYk9WTXpRgpdIiIiPSgzt8wVuIAWA5evj5X7r5jC0PgQLEctgCp9l0KXiIhID9qcXuj6HBcR4Fr+4bYLxjExJZrs/HIGRQVpr8N+SKFLRESkB9gdDsqr6jhY6FxZ/k+3zCY6PIA3v9hFdZ2dqUYMFoulxUHz0j8odImIiPSAt77YzRcbsl3HYcG+AFxx2khiYkLIzy/zVNWkhyh0iYiIHENtXT3b9h1mybpM4iICOGvGYIIDfcnKLWPssMh2jbn6essBt8CVkhiGj7deHw40Cl0iIiJHOVxazQMvf8vwQaHsyip2le/JLmH11kOu41svGMfu7GJq6+ycMnkQQ+ObvxosKK7in5/sBCAq1J/C0mounZ/S/Y2QXkehS0REpInvdxfw3KItAG6BqyULP95Orc0OwFebDwBwzVkGJ05IwOEAiwXue2mN6/o/3TpbsxEHMIUuERGRJt5atrtZ2fwpiVx1hkFFdR0bzHwOFlawdF2WK3A19e9PTV771CQmIoDbLhjnKn/sppkKXAOcQpeIyAD23c48Xv5oG9ctGMXssfFYrRbsDgcFxVVYLRaiwwMA2LyngL0HSjl79hD8fPrvWKScggryiquwWOCV++Y5y/IrSIwJAiDI34eTJg5iT3YJS9dltfocB5BXVMWT/9kEwOWnppIQFdTt9ZfeTaFLRKQf2GDm8fbyPRSV1fDrH08nMTqIg4WVxEcGYrU6e1eqamx8k3aINz7f1ez+hYt3sHDxDu64aDwfrtpHVl45AX5e/O2ukyirrOPZ95yv22pt9ZwyOZG4iMBO1TP9QAkxYQGEBvl2vrFdJG1fIW98tovrFoyivMpGgJ+X63XiZaemunqlkmKDm90bHe7v+pySFMYvLpuEj7cXz723he/3FLjOVVTbWn2GDDwKXSIivZyt3o7D0fI5h8PBx2sy+O9Xe11ljyxcxwnj4vkm7RCTUqK58+IJPPmfTezIKGrzu/72/lbX56qaevbklLB172FX2dJ1WW49PHMnJHD92aPb1Y684ioe+/cGAv28+cudc/H2srJt/2HeWrabuy+dRESIX7ue0157ckpYuHgHc8fHM39KEgF+zl95ZZW1vPhBGjsznQHr6y0H+SbtkNu9owZHHPPZ4cF+/OWnczl0uJKUpDCsDQHtzosnUFJRi5fVwtvLdrO64bnJCl2CQpeISK9jdzioq7Pj5+vFvoOlPPvuZhzAKw+d3uza7PwKt8DVqDFEfL+ngN/+33oyDrW+BtR9l0/m+f9udfXKNPXhqn1s3996WFu15SA/PGk4YcHHDkx1NjsPNAwor6yxceezX1NdW+86f8/zq1l4/zwsFguHS6vx8vM55vPacrCwgj+8tgGARSv38unaTE6cMIipRgwff7PfFbiAZoELnLMM2xIa5Ntij11YQ9mc8Qmu0BUccHztkf5BoUtEpJfYmVHEnxrGAEHzMLRhZx5DYwKps9mpqq0nKtSPD752Bq5TpyZx+amp3PinFc2ee3TguuDEYa69/5667QQiQ/35610nsXjNfhatdD7vjOnJfLY+yy1weXtZsdU3HzieX1LdZuh66B/fuh03DVyNbn/mK6aOjHEFlVcfmH/MZ7Zk6bpM3l6+p1l5RbWNT9dl8um6TFdQeuKW2Sxamc66HXlu114ybwSB/sf/63FEYijDEkIYmRx+3M+S/kGhS0SkFyivqnMLXIDrOCLEj6KyGv702nckRgdRXl1HSXmt27WNg+BPnZrEsiaLcDb1zB1zsFgshAb5Mm9yIhXVNiKb9OgsmDWEpJhgUpPC8ffz4rP1R14jRob6cdn8VF74II2rzxiJj7cXhw5X8sm3GVTXNu8hO1rjps4zRsfy3c587A3vSy85ZQTvfpkOOIPY6ia9Tna7wzUerS2ZuWVs3VvoCo2NnvvZidz57NduZaUVtQT5exMTHsCl81LcQteQ+BAWzBzSru9si4+3Fw9fO71LniX9g9XTFRARGcgcDgffpB10Cwa+3u7/a14wc7Drc05BRbPABTAsIQSA8+cOc5X95LwxhAT6MH54FM///CTCgv1cvTwhgb7ER7oPhrdaLExMiSbQ3xurxcIPTx6Or4+zLkZyONNGxfL8z09i3pQk5k5IcL1Gq2mh16opM9PZWxYbEcCN545h8sho17mxwyJbvS+/uOqYz23qhf+mNQtc4Hyt9+vrmgefxvFjkaH+vPrAfBKjnTMLQ/QaULpRmz1dhmGMAB4FNgJJQKFpmr8zDCMSeBzYC6QCD5qmmdtwzy+AUCAC+Mw0zY8ayicBtwP7gFjgXtM02/4nkohIP2S3O/jXpzv5estBV9nTt88hLNiXG5848powKSYYP1+vVsPNHReNd820C/L3ZvqoWIIDfZg1Jp5ZY+I7Xb9zZg9l/pQkvt2eywnjnM9pHIwO4OfrXDqipq710JWdV84Tbzp77ErKa/H2spJX5AxTMeH+DI4L4Xc3zOCRheua3fvV5gNcMq/tlds/X59FXgsBbd7kRMDZe/X4LbMpq6zlyTc3UWuzc8v549yubex5a2/PmkhntOf1YiTwlmmaHwIYhrHdMIzFwE3AF6ZpvmMYxg+Ap4CrDcOYCcwzTfNswzB8gO2GYXwFlACvA6eZpnnIMIyngWuBhd3QLhERj8nOK2dzegEHCiqZNTaOd5bv4ZJ5KUwYEeV23eb0Alfguv3C8Uw1YlznLBZcMxaDA3wID/Ilt7Z5sLBaLEwZ2fQ+C7deMK7ZdZ0V4OftCi9Ha1yvq7UwmH6ghMf+vcF13FjPi08ZwTPvbObm85z1TIpxn9k3f0oiyzfmsGRtJhefMuKYC4oWldXwn4bFTE8YF8+CmYMZFB3U7J7Y8ABiwwN46d5TWnxOVKg/BwsrKWx4DSrSHdp8vWia5vrGwNXkngrgHKBxb4PVDccA5zaWm6ZZB+wATgKGAwGmaR5q4R4RkT6horqOOpszZJRV1jZbhuGlD9N45NV1LFq5lzXbDvHMO5vJKajgL+9uZu+BUrdrs/LKAQgJ9GFyarTbuUtOOdLDExzow/TRsS3WZ3hi873+ekpjT9faHXmuV4FvfLbLNbh/fZOxUuHBvlxzlgHA+OFRvPrAfIYPOlL3y05NdX0+Z/ZQ1+eWZlQ2Kimv4Z7nV7s9IzEmuFOrvo8a4lwiIjYioMP3irRXhwbSG4ZxIbDUNM2dhmHEAo1TYkqBCMMwvHG+NtzR5LbShrL8Jtc3LT+miIhAvHtgJ/aYmJBu/47eYiC1FdTe/q4n21tns/Pz3y9tPoh9fAIPXjeDnPxy16BsX29rsy1iHv33d7z92Nn4+3pjtVpcMwj/cNtc4uLcw9M5J43gnRXOWXhDkyO5cUgUk0fH8+bSnYQF+3HqtGSefH0D918znRgPrXQeX1IDOPcnfPqdzfz5ZyexbKNzEP8NF0xwG4j/25+cQNKgsFafdeXZY7jkdIOqGhthwX6cOWsIS7/NwMvXu9U/4zU7j4S6+KhAhg1ufXxYW646ZyzRkUHMnTiozZmY3UX/7fZ/7Q5dhmHMA+YBdzUU5QEhQDHO8VtFpmnaDMNoLG8U2nBta+XHVFRU2d4qdlpMTAj5+a2vYdOfDKS2gtrb3/VUe2tq63nj812s2nqwxfNrth7ETM/nf9/sB+C8OUO54MTh5OSXs3hNBlv3Frp6bH700CeMTA7n0iZjlQK8OGY7iosqAJgxJp5hMUcC1qsPzAe73WN/5lWVNa7PeYcruerXn7qOn3/nyEzMv//iFLy9rO2uZ35VrWvAf9aBEgK8Wu652pFe6Pp8+wXjjvvnMGNkNLVVteRXNZ+o0N30327/caww2a7QZRjGOcCJwM+ABMMwhgCLgdlAFjCn4RjgY+DXDfd5A2OAxjFdVYZhxDe8Ymx6j4hIr5NxqIyXPtpG7uHW//GXGB1ETkEF2/YfZuX3Bwj082bBLOeSA4kxwfzkvLHU1NZz659Xuu7ZlVXM4jX7ARjZZDXzoz156wnU2o49M9CTQgNb38qnsZdrwazBeHt1fKJ8SMOzi8udwa6iuo6i0hoSY5zjtd5attsVgv9y59xj1kWkt2jP7MWpwNvAd8AKIAh4HngQeMIwjJHACOBeANM01xqGscIwjD/gnL14t2maxQ3Pugp4zDCMDMAL+FfXN0lE5PjZHQ6WrM1oFrjuvnQi44ZHUV5VR1llLd/vKeDdFen885OdAIwbHtlsQ2g/Xy8eu2kmD/1jrats027n/ny3XzS+1TpEhbW9KrontWf/xKOXpWivxtD10ofbGDM0kmfe2cy+g6XcfuE4BseFuL26DPDVkpPSN7T5N9U0zQ1Aa5tG3dTKPU+2Uv49cEO7ayci4iHPvP0925qsxj5vciJXn2m4joMDfAgO8CEj1/0VyYUnDm/xeQlRQTx12wnc+8I3buV9eXsYH+/mPVjP/HQuyzZk8fE3GYBzj8LOCA0+Eug+W5/JvoPOSQiL12Swv8kK+6FBvi3WQ6Q30j8PREQa1NbV852Zh8OBK3D97OIJjEgMazUczRgVxz8/2Umdzc7k1GjijtGzExnqT3JssGvW4pD4kE7NtOutRiaFERbkS0piOOAMXYPjOjdYekLKkdmcjQEOcAtc44dHcdclEzpXWREPUOgSkX6vpLyGQH9vfLy9qLPZefyNDQwfFMaVp4+kvKoOM7OYzXsKmg2U/8l5Y5iYEt3KU52sVgsv3nMyq7ccZHKT9bJaMyg6yBW6po9qcwJ3n3H5aamcPi0ZcM4kbBQS2LmePP9jvDIM8vcmyN+Hn186sVPPFvEUhS4R6dfe/XIPS77NdB0nxQSRnV/BvoNlhAX58uGqfdTbHS3e297V3K0WCydOHNSua4cPCmXt9tyG58e1656+ILDJSvXRof74+lipq7O3OkmgPe67fHKz/SjBuXbXVKP/BFYZOPQiXET6pOLyGups9dTZ6ikqq2nxmu925rkFLoDs/ArX5/e/2ttq4LrjGAPcj8fUJr1h4SGeWQ+qKzUGx+TYI0N/rVYLD18zjadun3Nczx41JIIh8UdeT557wpGNqKe0o1dRpLdRT5eI9CkOh4N/fLydb7flNjv35zvmEB7sx479h3nyre/b/cyLThruWvE9LiIQh8PRbWOtIkP9ueGc0QDH1QvUW1x/zmguPGk4MeHuK7knxrQ2/6pjfn7pRP65eAcXnzICq9XiGt8Vp5XjpQ9S6BKRPmXtjtwWAxeAmVnMzDFxzQLXFael8tn6LM6YnkxUqD+hwb6uPQGTYoI594Shbtd39+D2OeMTuvX5Pcnby9oscHWl0EBffnZJ87FbTTfeFukr9LdWRHqtmtp6lm/MxmKx4OVlwW538PbyPXh7Wbn4lBF8sSGbguIjm0CXVNS6lhYAmDshgevPdvYqndYwyLvR8EGh7D1QysQU902opXe77YJx7Mkp6fQAfRFPUugSkV5p2YZs3vh8V4vnYiMCOGN6MleePYY1m7LJLijn35+avLVsN2ENC3aeM3sIPzx5RKvPv+ZMg/U78zhvzrBuqb90j2mjYpnWj2Z9ysCi0CUivYbD4aCorIagAJ9WAxfA7LFHZv2lJIUR3KTXo6TCuW/e/ClJx/yuwXEhnV5DSkSkMxS6RKTXeHv5HrftXQDmjIvnh6eM4Pn3t5J+wPnq8OiZa0dvNTNxRBQR/WBmoIj0LwpdIuJxDoeDX/79W/KKqtzKf3rReNeCow9dM43d2cXERQa2uLnxK/fP48YnVgBga2UZCBERT9I6XSLS4yqr63A4jgSjXVnFrsCV0LCaeWJMULMV3lOTwlsMXOBcfqFxGYGETm6yLCLSndTTJSJdrs5mx+5w4Ofj1excaWUtv3jhGyanRnPL+eMAZ+gCOGFcPDeeO4baunqs1o4v2/DAlVNI23eYE8a1byV5EZGepNAlIl2mzlZPZbWNZ97dTGZuOTf9YAyzxsS5rXt1IL+COpuddTvymD4qn6TYIP779T4AzpwxGADfFsJae4QF+/WrNbBEpH9R6BKRLmFmFvHEm+775P3jf9sxM4u5bsEowNnL1XQvvef/u9Xt+thuXGRTRMTTNKZLRI6pzlbPik057Nh/uNVrdmQ0D1yNvtp8gDqbHYAVG3NafcaN547Gz7dzPVwiIn2BerpE5Jgee20DmbnlruNn75xLSKAvmbllxEUG4ufjxdrthwAIDfTh/iunYLc7SIgK4vE3N7Inu4Sbn/qSy+ansHrrQeczb5pJXGQgry01Wfn9AQDGDI3s+caJiPQghS4RaZXd4XALXAA/e26V6/PssXHc9IOxmJnOgfC/umYa0U1eERrJ4ezJLgHgreV7AOeK4glRQQBce9YosvPKySmoIDhA27qISP+m0CUiLXI4HK51rwAGRQdxoKDC7Zo123KZPiqO3KIqYiMC3AIXwA9OGMriNRluZRef4r41zy8un0wy9hMtAAAgAElEQVS93YG3l0Y7iEj/pv/LiUiLGmcUAjx0zVR+fd30Fq97btEWAG4+b2yzc74+Xrz6wHwuOzUVgHsvm9RssLyvjxcBfvr3n4j0fwpdIgI4B8x/uSmHqhobpZW1fPzNfgD+8JNZjBgUho+3ldsuGNfivWOGRjA0vvV9DM+YnsyrD8zXuC0RGdD0z0sRAWDpuize/2ovX27KITPvyDiupvsaThsVy59umU1UmD83NHn1eM+PJrmtxSUiIs0pdIkMUFvSC1yLlK7fmecqbxq4Lpuf0uy+xnFbKUlh7MkuYfSQCAUuEZF2UOgSGYAWrUxvNsD9aL/58XQGx7X+yvC+yyfz+XdZTB8V29XVExHplxS6RAaIw6XV5BdXYbFY2gxcwDEDF4C3l5UFM4d0VfVERPq9NkOXYRjxwKPARNM0pzeUXQfcAlQ3XLbQNM3XGs5dBUwG6oF00zRfbigfCjwM7AGGAveYpum+AJCIdKk6Wz3pOaWs2nqQb9IOuZ279YJxRIX642W1MKRhELzd7uCj1fuYOSbOE9UVEenX2tPTNRf4EJh0VPllpmnub1pgGEYScC8w2TRNh2EY6w3DWG6a5m7gJeAR0zTXGYbxU+B+nCFMRLrBlvRC/vLu5hbPBQf4MCklCh9v9213rFYLF5w4vCeqJyIy4LS5ZIRpmu8BZS2cusMwjHsNw3jEMIzGeeBnAhtM03Q0HK8BFhiG4QPMA9Y3lK8Gzjm+qotIa7akFzQLXJc0WZT0+rNHNwtcIiLSvTo7pmslsNg0zXzDMM4G3gVOBWJxD2ilDWXRQFWTMNZY3qaIiEC8e+CXQ0zMscev9CcDqa3Q+9vrcDios9nx9Tn+v+cl5TUsWZ/Fu8t2Y7FAcIAvsZEB3HHJJFKSwtmUXsje7GLmTk3uNwuS9vY/3642kNo7kNoKau9A0Kn/65qmua/J4XLgI8MwvIA8oOkc81CcY7gKgADDMCwNwSu04do2FRVVdqaKHRITE0J+fkudef3PQGor9I32fvzNft7/ai+pSWGcOGEQq7YeZOywSDIPlXHC+Hgmp8a06zmfr8/iP8t2u44vnZfCmTMGu47z88v4xY8mUWezU15aRX8YUNkX/ny70kBq70BqK6i9/cmxwmSnVqQ3DOOPhmE0BrZUYJ9pmvXAUmCqYRiNi/bMBpaYplkHrAAa9xGZAyzuzHeL9CflVXUsWZsJwO7sEl79ZAe7sor571d72bArn78u2nrM+x0OB/c8v5q7/rqKrXsLXeU3nTuG06cnN7vex9tKoH//6OESEelr2jN78WTgaiDBMIxfAU8Dh4AXDcPYB4xvOI9pmtmGYTwFPGMYRj3wSsMgenDOdnzEMIwzgMHA3V3eGpEuZHc4sHbzop8vfpBGVY2NAD8vqmrqW7xm8Zr9bE0vZP7UJGaMPjKrsN5uZ+HHOygqqwEgbd9hAJ667QQiQ/27td4iItJxbYYu0zRX4hzD1dSzx7j+deD1Fsr3A9d3sH4iPWp3djGfr8/iOzMfXx8rj904i6gwZ4CprrVht3NcPUU7M4p48cM0yirrmDAiih0ZRQBceOJwkmOD+e9Xe7nu7NE8/Mpa6u3OIZCLVu4FYFd2CTNGx7F1byGvLt5BSUVts+cH+nsrcImI9FJ6zyDS4JWPt7utZVVbZ2f5pmzOnDGYjbvy+fenJl5WCy/cfVKHZ/45HA5e/2wXKzbluMq2pDtfB44aHM5p05yvAh+4aioAL//iFIrLarj3hW/cnnPocCXPvNN8GYhxwyJJ23eY5DYWNBUREc9R6JIBz253YHc42LQ7v9m5Jd9msuTbTNdxvd1BRm45KYlhx3xmna0eH28vSspr2LS7gH8vNV3n5oyPp7i8lm0NrwObDnZvZLVYiAz158Grp1JbV8/H3+xnZ2YxT7y50XXNmTOSWTBrCKGBvhwurebNL3Zz2yWToL7l15QiIuJZCl3SaWl7CwkO9GFofKinq9IpRWU1fLImg2Ubs11lfr5e3HjOaCamRPPB1/v45Nvm2+W8ttTkt9fPaFbucDhwAO+v3Mtn67O484fj+fNRvVI3nze2Q6u9N4a7tL2H2ZlZTEl5Ld5eVp69c67bkg+Rof7ccdF4YiID++2MIBGRvk6hS9y0d/D4jowit0Bx2ampnDo1kdo6O0CzNaDWpB2isLSac2YPobCkmqgwfyzdPEi9NZm5Zbz04TYOHW6+HMm5s4cw1XAuIRcV6ud2bsKIKLakF5KVV87a7bnNwtPf3t9K2r7D1NmcP4OjAxeAMTi8U3VeMGswn65z9rhNTo3uN2tsiYgMJPo/twBQZ7Pzpzc3kl1QwW+um47N7iAxOsh1vqCkim+35RId7s/IpHCe/M8mt/vfWrabz9dncrishsToIH53w0zXuaXrMnl7+R7Auc3Me1+mc9rUJK44fWTPNK6JnIIKfvPP9W5lV50xktc/2wXAObOHusrnTkggv7iaU6cmERLog7e3lW+2HuLVT3awaGU600fHYmYUMWxQKGl7D7Npd8Exv/up204gPNjvmNe0JiTQl9svHMdby3azYFbz15EiItL7KXQJRWU13PP8atfxL//+LeDcn+/p2+dQb7dz34tr2nxOYalz6YLs/AocDoerJ2vZhiOv7977Mh2ALzZkc8GJw3tszag6Wz0vfrCN7/ccCUZxkYHcc+lEosMDcDggLiLA7R4fby8unZ/iVjZ3QgI7Mg6zZlsuNz6xAqDF5R6mj4pl/c48Jo6I4uDhSlISw457VuFUI9bVCyciIn2PQtcAl5lb1qznp1F5VR0frd7H15sPtHj+9zfMICTQFx9vK8//dyvb9xe5zuUWVREfGUhNbT0FJdVEh/lTUFLtdn9ReU2Pha7XP9vlFrgeu2kmCVFHevJOnZrU7mdNNWJZsy3XddzS+loXnjScK08fib+vFz7eVo+9ShURkd5DoWsAOlhYwSML17nWgQLwslp48Z6T+WRNBh+sOrLL0+I1RwaS//6GGRwsrOSFD9IYHBdMYkyw69y9l00mbW8hG3cX8OWmHB5+ZS1nTE92rbY+cUQ0m9ML3ILX+h25JJ44vEN1tzscZOWWkxQbhJe17Q0V6mz1PPHGRsysYgBSksKYlBLtFrg6KjGm5XtnjI5l9th4qmptxEcGdvr5IiLSPyl0DTALP0rjg5Xpzcrv/tEkvL2s/GDOUM6ePYSKahs//+sq1/nbLhhHYowzaL1498nQQsfNuOFRDBsUypebcqi3O1yBC2BQTBBXnjGSmtp6KqrruO/FNXzybQZnzRyMv2/7/xqu3JTDa5/tYt6URPx8vLjopOF4e7UcvorLa7j1zyupqXX2RF18ygjOnjWk3d/VmpjwAE6ckMDI5HA++HovhaU1zBgdyy3njzvuZ4uISP+l0DVAfLv9EN9uy3UtyAkwMjmcXVnFTBgRhZHsnFVnsVjw9rIQFuTLeXOG8tHq/QBMG3VkLJGfb+sLgwb5+3DB3GFuvWUAY4dFuu718/VizNAI0vYd5mBhJcMS2r/kRGP9V2x0LjL66dpMXrznZPx8vFiz7RAbzXxuOHc0jyxc59ar9uBVU0lJOvbaWu1ltVj48dmjAcgvrmLFphxmj43vkmeLiEj/pdDVz+0/VMrrn+1i74FSV9ng2GCuPtNgRGIYdocDC7Q45mhIw+rm8yYndug7fzBnKP6+Xnz2XRZzxycwfFAoseHug9T3HXTW56vNB9oduqprbWxuEhobLfk2A7vDwcffOF+FFr9V4xa4XrlvHlZr94ypuuDE4VzQwVekIiIyMCl09RMFJVXkHq5izNAIV4BK21fIn98+slbUglmDOXP2MEL9jvRUHWtNrskjY3jo6qkMTejY1jIWi4UzZgzmjBZWWm90wYnDeePzXSS0c+xTTV09Cz/e0eK50so6vmyyvU56k4D5x9vmdFvgEhER6QiFruPkcDjYYOazJb0Qh8PBFaeP9MjClc+9t5Xs/HKsFguv3D+PPTklboHrF5dPZvSQCGJiQjq0YvmINra76azYhuUZ6urtrrJ3VuyhsKSam34whteWmswcE8eYoc7Xkv/8ZAcbdjXfpgdg275CfLytrkVJG/31rhMZmhypFdpFRKRXUOg6Tu9/tddtht/qtEP84rJJjG4ICz3BbneQnV/u/OxwcMPjy2mclzhhRBQ//eH4ds3060l+Ps7eti82ZLNo5V5uPm8snzYMvK+ssbFt32G+3nKQZ++cS3CAD+t25LnuvfzUVHZlF3PtWaO4/6VvyC92vko0ksNJjAliecN4L63aLiIivYl+K3VSvd3O3xZtbXGM0dvL9/CbFvbm60q2ejuff5fFR6v2Exbk63bO0eTzuScM7XWBCyA6zLlQaEl5LQAvf7TNda5xI2iAjNwyfL2PvA6dMTqW06cnc/r0ZAAuPHE4b36xG4CQQB8umZdCkL8P/n5e7drOSEREpKcodHXQrqxiHn9jo1tZQlQgp09L5t9LTQAy88r591KTq88Y2S2LYtrq7Ty3aAtpe53hJK+4CgvOZR+efvt7t2uPHsDeW7R3dfY/v72ZpIZ1sa48fWSzRUwbZ0WCc8kKPx8vLjxJA9tFRKT3UegCqmtspO0tpLi8likjown092nxupYC14NXTyWlYdzTCePiuftvq6mssfHlphzOmzO003vttaSy2oaZVcTfP9pOTd2RVdCvPH0k00fHEhroy72XTaKorIakmGAsFgg9qhesL8rOrwBgzNCIZuea/nxTu2hJCBERke4w4ENXXnEV1z++3HWclZfM5aelNrtu9daDLFx8ZPbc7ReOZ1JqlNurO18fL+odR17uffX9Ac6bO6xL6plbVMkvX/7WreyeH00iPNjXbWX4MT04lqw73PnDCTy3aAujBoezM7PY7VxcRPOZjgF+3lx+WiqFJdVaBV5ERHq1AR+6ljZZNR1gd3Zxs2vsDofr1eFJEwdx+WmproHgR2tc/RxgybpMfjBnqOsVY0l5DeVVdW4hqVHTUHXDOaOZMz7Bda64vKZZ4Bo/PMrt1Vp/cPq0ZCalRvPKfc7Zl017FWePjWt16YfTpyX3VBVFREQ6bcCHrjNnJDN3chK22jr++PpG6urt5B6uxMfbSnCAD//36U6+bdjceMKIKK5bMKrdz66prWeDmU9sRAD+ft48snAttXX2ZqFqxaYcXmsIdQCfr89ynd+x/zDvrDiybc/YYZHccv5YfL173+D4jvL1sVJbd2SZhxGJzkVSrVYLKYlhnDVjMJ+uc4Zir1a2+hEREekrBnzoio0IdK1dFRbkS05+Bb/8u7NX6dJ5Ka7ABXDjuWPafN5dl0zkkzX7iY8K4qvNB3jhg7Rm12ww85kzPoEt6QW892W6a8xSo8y8chatTOe0ack8+ZZzYHxIoA/P3DG3Xy30+exPT2TT7nymjYqlzmZ3W+LBarVw6fwUcgoq2Lq3ED/v1rceEhER6QvUfdBEasP+g43eWbHH9fnBq6YSHNDyAPumJoyI4oGrplJcXtPqNd/vKWDfwVL+8u4Wt8B1+4XjXVvvLF6TwbrtRwLfj88e3a8CFzj3YZw1Nh5vL2ura2rdeO5oTpuaxAUndc3YOBEREU9R6GriJz8Y02yG3GlTk3j1gfkd3iw5MqTlWYuTU6MB+P2/vnMr9/ayMNWI4YwZR8Yn/WeZc/2pq88YyaSU6A59f38REujLFaePJKiVGaUiIiJ9hUJXE95eVm6/cDxTjRhX2Y9OTenUs350aipzxycw8qiwdu1ZLY8Je+KWEwCYPTaeF+8+2e3coOigTtVBREREeo8BP6braAF+3tx+4Xi27T9MaKBvp1dz9/Px4vpzRgOwM6OIP/1nE7+8agqhQb5EhfpTWOrcuubp2+cQcVSvmJ+vF3MnJLBqy0GAFmc7ioiISN/SZugyDCMeeBSYaJrm9IYyf+ApIAdIBR43TXNXw7mrgMlAPZBumubLDeVDgYeBPcBQ4B7TNMu7uD1dZmwXrnc1akgErz4w/8izh0Xy1eYDAK2OE7v+7NFct2CUtrIRERHpJ9rTjTMX+BBo+tv/LiDTNM0/As8ACwEMw0gC7gXuNU3zPuBGwzAaVxp9CXi54Z404P6uaULfc96cocydkMD9V0zG5xhLPyhwiYiI9B8Wh8PR5kWGYZwCPGWa5rSG46+BB03T/LrhuBRIAi4BTjBN84aG8udw9my9CJQD/qZpOgzDmAK8YprmlLa+22ard3hruQARERHpG1rtMensmK5YoKzJcWlDWWvl0UCVaZqOo8rbVFRU2ckqtl/jOl0DwUBqK6i9/Z3a238NpLaC2tufxMSEtHqus7MX84CmTw1tKGutvAAIMAzDclS5iIiIyIDQ2dC1GJgNYBjGeGCzaZqlwFJgapNwNRtYYppmHbACmN5QPqfhGSIiIiIDQntmL54MXA0kGIbxK+Bp4FngqYbjFOAGANM0sw3DeAp4xjCMepzjtnY3POoW4BHDMM4ABgN3d3lrRERERHqpdg2kFxEREZHjoxXpRURERHqAQpeIiIhID1DoEhEREekBCl0iIiIiPUChS0RERKQHKHSJiIiI9ACFLhEREZEeoNAlIiIi0gMUukRERER6gEKXiIiISA9Q6BIRERHpAQpdIiIiIj1AoUtERESkByh0iYiIiPQAhS4RERGRHqDQJSIiItIDFLpEREREeoBCl4iIiEgPUOgSERER6QEKXSIiIiI9QKFLREREpAcodImIiIj0AIUuERERkR6g0CUiIiLSAxS6RERERHqAQpeIiIhID/D2dAXakp9f5uju74iICKSoqLK7v6ZXGEhtBbW3v1N7+6+B1FZQe/uTmJgQS2vn1NMFeHt7eboKPWYgtRXU3v5O7e2/BlJbQe0dKBS6RERERHqAQpeIiIhID1DoEuli5bUV1NvrPV0NERHpZXr9QHqR41FdV82B8kPY7Da8rd74evkSHRDZpd9RZ7dRWFWIn5cfb5nvk1a403UuNjCa2vo6Kusq+ZFxIbMSpnXpd4uISN+h0CX9TrWtmq+y17DqwFoKqw+3eM3M+KlU2aqpra/lgpRzSA4Z1KHvKKkpZU/xPnYVp7Mq59tWr8urLHB9fm3HO8yIn4LVog5mEZGBSKFL+p3/7V3Kl9mrAUiJHEq4Tzg2ez219lpyK/IorC5i7aENrusfX/8XkkMSOWvIfFIihhPsE+Q6l1eZzwd7PgGLBR+rN7uL9uLn7UtxdQm19rpm3x3lH8GVoy4hNWI4+0oyqbJVsbckg6UZy13Piw+K6+afgIiI9EYKXdJvOBwO1h3a6Apc90+/k6nDR5OfX+a6pspWxdaCHQwNHYyvlw+fZ3zJl9mrySrL4R9prwHwhzkPE+Dtx66idF7d9gY19bXuX9RwmBw8iKzyAw33/Iowv1C3y0aEDwVgXPRobHYby7K+orq+phtaLiIifYFCl/QLVbYqVuWs5YP0T7BarJyceAKDQ5KaXRfgHcCM+Cmu40tGnk9K+HAWpr2OA+c6vA+u/r3bPanhw5kUM54hocnEBkbj6+WLl8WK1WLF7rADtPnK0M/bD4CM0myGhg4+rraKiEjXqqys4Nlnn8Zut/PQQ7/ptu9R6JJ+4dmNL7t6nR6Y/jMSgxPafe/k2PH8bf4TVNZV8q/tb5NRmkVMYBRhvqFMj5/MxJhxrd7b3vFZ4b7OXrCV2as5OemEdtdNRES6X2BgEGeeeTZLlnzcrd+j0CX9QmPgujDlnA4FrqYCfQK5deKPu7JaLjMSpvKmuQiLBtGLyAD0/p6P2ZS31XXsZbVQbz++Xf4mx47nopRzWz1vs9n47W9/RWZmBg8//Du8vLx47LHfcOmlV7Bx43qSkweTnZ3FggXnMmHCJLd709K28u67bzJy5Ch2797FLbf8lPj4+OOqL2idLukHcsoPAjA6ciSnDT7Zw7VpmY/VmzDfUGx2m6erIiIyIHh7e3P//b+ioqKc5OTBJCQMYtKkKaxe/RUzZ87myiuv5aabbuXXv34Qh8M9AAYEBHDzzXdw5ZXXMm/eqSxa9HbX1KlLniLiITnlB/nDumcAGBE2zMO1OTYfq7dCl4gMSBelnOvWKxUTE+I2yam7BAcHM2vWCSxb9hm1tbWcccZZ/O53j3DFFdcAEBkZRXl5OcXFxW73+fn5sWjRO4SHh3PgwAFstuaz1TtDPV3SJ9kddh5f/6wrcAGcPqR39nI18lboEhHpcRdddCnvv/8ue/bsYuTIUaSkpJKTkw1AYWEBISEhhIeHu93z/PPPkpKSytVX/5h5807tsrqop0v6pB2Hd5FVlgPAuKhR3DDuKrytvfuvs0KXiEjPGz58BIGBgUye7NwR5I477uLvf3+B7OxMsrOz+M1vHqOqqpKlSz8hPX03W7du5swzF/Dee29z8OAB8vJySU/fzc6d2xk1asxx1aV3/5YSaUVhlXOl+UtSz+eU5Dkerk376PWiiIhnPPfcS67P0dExPPjgr5td88tfPuJ2fMopXdfD1UivF6XP2ZK/jbd3fQBAXGCMh2vTft5Wb2yOetfaXiIiMrAodEmfsy53EwDT4ya7Vn3vCxpff9bb6z1cExER8QS9XpQ+pd5ez67De/C1+nDtmMuwWCyerlK7NYYum8OGDz4ero2IiPQ09XRJn7L6wDoqbJWMjx7TpwIXOMd0AdRpXJeIyICk0CV9yuHqIgCmxk30cE06rrGnq65eoUtEZCBS6JI+w+FwsP2wCdDiZta9na+XLwB19loP10RERDxBoUv6jMX7PiOn/CCDQ5KI8A9v+4Zexs/qDF019QpdIiIDkUKX9Amb87fx6f7lWC1Wrhj1Q09Xp1P8vBS6REQGMoUu6fVyK/L4+9Z/4cDBrRN+THJIoqer1Cl+3n4A1NTXeLgmIiLiCQpd0uttLdwBwKSY8YyJMjxcm85TT5eIyMCm0CW93poD6wG4OPUHHq7J8fHzUk+XiMhA1uHFUQ3DOA24CMgDHKZp/vao8/cD8cAhYCrwiGmaOxvOXQVMBuqBdNM0Xz6+6kt/l1mWzaHKPJKDB/XJwfNNNfZ0vWX+lzmDZnq4NiIi0tM61NNlGEYg8BLwc9M0fwNMMAzj6B0hg4G7TdN8AlgEPNlwbxJwL3CvaZr3ATcahpF6nPWXfm5VzloAzhg638M1OX4jwoYBYHfY2V5oerg2IiLS0zr6enE2kGGaZuP7kdXAOU0vME3zYdM0HU2eX97w+UxgQ5Nza4AFHa+yDCRbC7YT4hPMpJhxnq7KcQv2DWLB0NMAyC474OHaiIhIT+vo68VYoKzJcWlDWTOGYfgC1wK3d/TepiIiAvH29upgNTsuJiak27+jt+grbc0pPURpbRmTE8YSFxvW6ef0pvbOdIxnyf4v8Pa3dFu9elN7e4La238NpLaC2jsQdDR05QFNf0qhDWVuGgLXi8BDpmmmN7k35ah797T1hUVFlR2sYsfFxISQn1/W9oX9QF9pa0lNGQ+u/j0AI0NGdrrOva29lWXOLYCKysq6pV69rb3dTe3tvwZSW0Ht7U+OFSY7+npxDTDEMAy/huM5wGLDMCINwwgFMAwjAHgZ+LNpmhsMw2hcyXIpMNUwjMZdimcDSzr4/TJA7CvNcH2elTDNgzXpWv7e/gCsPbRBS0eIiAwwHQpdpmlWArcCzxmG8SiwxTTNZcADwG0Nl72BM4w9bxjGlw3nME0zG3gKeMYwjKeBV0zT3N0lrZB+p7YhkJw5ZD6+Xj4erk3XiQ2IJsgnkIq6SnYXpbd9g4iI9BsdXjLCNM3Pgc+PKruvyeeLjnHv68DrHf1OGVjKasv51/a3ABgSmuzh2nQti8XC+SMW8ObORRRUHfZ0dUREpAdpcVTpVfIq83lg1e9cx0nBgzxYm+4xOCQJgOVZX1Nt00KpIiIDhUKX9Co7DzvnVnhbvXnm5MeICojwcI26XnJIIpNixlFYfZjVB9Z6ujoiItJDFLqkV3A4HKzIWsX7e/4HwN1Tbu1XY7mONi1uMgAfpi+h3l7v4dqIiEhPUOiSXuF/e5fy3u6PqLM7l1RIDkn0cI2618SYsSQHD6LeUc+63E2ero6IiPQAhS7xuNr6OlZkr3IdT4ubhNXSv/9qWi1Wbp5wHV4WLz7PWIHdYfd0lUREpJv1799s0idkl+e4log4ffApXDfmcg/XqGdE+IczNW4iuZX5fHtwg6erIyIi3UyhSzzute3vAHDOsNO5IOVsLBZLG3f0HzPipwDwxs53WbJvmYdrIyIi3UmhSzyurM65J/rcxFkerknPGxWRihHh3B1r8b7PsDWMaRMRkf5HoWuA+dhcxqLd/2NF1irK6yo8XR0cDgc19bUMDxtCqO/A2/zUYrFw5+SfMCF6LA4cveLPREREukeHV6SXvqveXs+ibYupqKsC4KP0Jdw//U7ig+JavcfhcHCwIpfimhJCfUOICYzGz8u3y+pUZ6/D7rDja+26Z/ZFEf5hAFTUVRLuF+bh2oiISHdQ6BpAvKxe/O3cR9metY/F+z5ne6HJJ/u+4IRBMxgZMaLZjMHa+jr+vPEFsspymj0r2CeIlPDh3DjuquMag/Xe7o8ACPQJ6PQz+oMgnyAAdhzeRWJwgodrIyIi3UGvFweYIN9AhoYO5rzhCwDYkLeZv37/Dz7d7z6I2+Fw8L+9n7oC16nJJ3FCwnTX+fK6Cr7P38r+0sxO16WmvpY1B78DnLMWB7Jo/0gAluxbhsPh8HBtRESkOyh0DVBJwQmcPfQ01/GB8kOuz2W15fx+7dMsz/oagAdn/JyLUs/lytGX8PRJv+eWCddxzrDTAfho79JOh4QqWxV2h51hoYMZHJp0HK3p+6bHTyY2IJrq+mq2Fe70dHVERKQbKHQNUBaLhXOGn8EzJz8KwKb8razIWkVawQ4eWPU7civzAPjJ+GvdXnf5e/sxPnoMZw6ZT3RAFLuK9rCvNKNTdahp2Ox5UHD8cbam77NarAV/hjQAACAASURBVJyfcjYA6w5t9HBtRESkO2hM1wDn6+XLaYNP5ovMla7xVY0emnF3q4HIy+rFzPgpLN73OdsKdjI8bGiHv7vGXuuqg8DE6LFE+0eSVrgDh8MxoNYrE+kNlu5fzmcZX1JdX80tE64jPjCOqICIfr9DhvQchS7hwpRzGB89hvd3f4wDOxOixzE3cSYhvsHHvG9kRAqL933uCk8dVVtfB4DfAJ+52MhisZAYnMDmgm2U11W0+fMXka6zKW8rH+391HX80pb/AyDKP5IZ8VMI9PZnTuKsLp29LQOPQpcAkBI+jPum/7RD90T4hQNQXFPaqe+sqVdP19Ei/J0/06LqYoUukTbU1texrySDxJAEghtmAHfW8qyvADg5aQ71dht2h53S2nLSCnewZP8XACza8zHjo8dw8/hr1RMtnaLQJZ0W6R9OkE8gOWUHOnV/rUJXM9EBUQDsKk4f8JMLRNry3z2L+SrnGwD8vfy4Z+rtriERdoedoupiIv0jmgUku8OOw+HAy+rlKqu0VRPkE8ilI893u3Z/aeb/t3fn8VFVZwPHf7Mlk31nCSQQSDjsOyKLK9Zq3SpdtNa3avW1bm3VWqyvVVzrWru8tlVbbW3V+qpYrQsKilRAJICsIRwICQkkZN/32d4/ZjImhi3LrHm+n48fM3funXke7syd55577jnUtTewoWwT+bX72FW9h4bORhlPT/SLFF2i3wwGA6lRKZQ2leF0Ofvc76HD4e5IH2mK9EV4IalrSqB/FbzHwpHziLZEBzgiIYLTxrLN3oILoN3RwcO5T5EQEc+ouJHsqy3A7nKQGJmA1RRJtCWa1Khk2uztFNQXYnPYsJqtNNtayEkcR3lLBSmeoVu6Gxufydh4mDVsGiuLPuLdolUcaiqVokv0ixRdYkBSrEkUNx6isbOpzwehrpauSJPFF6GFpPTYEWTEjeJQUym17fVSdAlxFO8WfshKz9iCl2ZfwNSUSXxyaB3ryzbR0NlIQ427y0NcRCw2p436jgYAChsOAu7W9diIWFpsrQDsry8EIDWqd9HVXUbcKABKmkqZljp50PMS4U+KLjEgXWeGaw6tY2n2hX3aVvp0Hd201MkcairlDzue54GFd2ExytdUiC55NXv54OAaAH4252bGJYwB4HsTv8U3ss5l45FcWmytzBsxi8w49yV6u9NOYcNBtlTsYFbaNCYm53gvOVa2VtHY2UxNWy0qOfu47901fE5la5Wv0hNhTo7mYkCmp01hdclaPi75lBRrMmeMXnjS237Z0iVFV3fzR8zm/aLVNHY2sa50I2dnnBbokIQIuK0VO3gh72Xv4xumX+0tuLokRMZx3tglvbY1G81MSMpmQlLvompYdBrDotPITsw6YQxWsxX4smuEEH0lg4+IARmXMIaFI08BYHP5tj5tK+N0HV1qVAp3zLkZgBX73yG/Zl+AIxIisI60VPQouGalTQvI5b2uE8QOe/+GyRFCWrrEgH1/0rf57EguLfaWPm3nvbwo43T10nVZBGB1yVompUwIYDRCBIbT5eT/9r3F+tLPvcuunXols4dND0g8RoMRi9FMu6M9IO8vQp+0dIlBkRiZgNPp7NM2cnnx2ExGE78781cAlLWUn2BtIcJTSdNhb8E1PmEsj592X8AKri4pUSmUNZfLxPSiX6ToEoMiwmShur22x8TZJ/Jl0SVDRhyN2WgmKz6Tps5mOcCLIafd3sG/D7hHiB+fkMVPZ/2ImCC4mzfVmoTd5aBd+nWJfpCiSwyKUbHpAPxxxwsnvY3cvXhiUZYoAJ7e/pcARyKE79S11/O/2/7M3RseJr92H7nlX/CDFbei6woAuGT8+T0GMg2kGM/I962e4SaE6Avp0yUGxVWTL2dH1W4aOhtPerLmTkcnBgwyJMJxnDl6EXtqNIUNB2USbBFSmjtbeCHvZUwGE9dP+wGWY4zH53A6uP/zJ7A53XOxfvUEIyEinoy4dJ/He7KiPSdCLbZWUk4wrpcQX9XnXzul1DnAUqAScGmt7z/KOt8FHgF+qrV+t9vyz4GuHogOrXXve3tFSLIYzUxOVuyuyaeqrZph0Wkn3KbT0UmEySKFxHFMSZnItNRJ7KrO57MjuSwceYr8e4mQcOf6L38a8mo1M9Om9ni+uq2WLyp3UNfe4C24uqTHjGDh2DkkGVN6bRdoMWb3Jc5X9Ap+Me+nAY5GhJo+FV1KqWjgGWCK1rpDKbVCKbVEa/1xt3WygCrg0FFe4gOt9X0DCVgEr0nJE9hdk8/jW57mnvk/IyEy/rjrdzg65dLiSUj0TCz+yt4VJETEMzV1UoAjEuLYihqK+WveKz2W/XnX3/mfU27zDi7aZm/j4dynvP06AW6ZeR0TEsfTam8jLiKWtLQ4qqqa/Br7yZiSMpF3i1ZxuKkMh9MRNJc9RWjoa0vXAqBYa93Vg3ADcAHgLbq01kVAkVJq+VG2n6aUuhOIAjZrrd/rR8wiSJ0xeiGbyrdQ0lTKkZaKkyq6ImW4iBOaO3wm60o3AnC4+YgUXSIouVwuVhev5e3Cld5ls4dN50D9QRo6G/lV7m+YkjKR8Qlj2Va5k05HJ8OiUlmacyHJ1iRvQRYXERuoFE5KZvxo5g6fyZaK7TR0NpJsTQp0SCKE9LXoGgZ0P/Vo9Cw7WY9prXOVUibgU6VUk9b60+NtkJQUjdns+zOJtLQ4n79HsPBlrmeNX8CL29/AEn3i97G5bCRExvn83z7U921a2nQ+OJRDftV+ml2NJ8wn1PPtK8k3OOyu2OstuLISM5iUls1l0y6mqqWG5Wt+TYutjbyaveTV7AUgOSqRpy64l4jjzL0arLmmJw2DCnij8C1qWuv5r5lLmTVy4JdBgzVfXxlq+ULfi65KoPu/Urxn2UnRWud6/u9QSq0DzgKOW3TV1fn+DpFgbcb2BV/n6upwF8jPbX6FzIis43aSb7d3YHKZfRpPuOzbH025htvW3k1exT5Ky2uP+UMVLvmeLMk3eLy87d8A/GjaVUxLnYzBYKC53kYU8Ty6eDlvH1hJalQySZGJOF1OcpLG01DbzpfdfHsK5lzHRY0DYEd5PgCPfPoH4iJiuXnGdf3u9B/M+fpCOOd7vGKyr0NGbATGKKW6BlZaBLynlEpWSh33WpJSaqJS6tpui3KAgj6+vwhyU1MnMSp2JM22Fv59YCVVrTVHXa/F1orT5ZSBUU+SxWgmKyGTytZqbvvP3dic9kCHJIRXeUsFBxqKSI8ZwfS0Kb1u9jAajFyafQGnjVrA1NRJTE+bQpRnHsNQlJM0jpzEcT2WNXU2k1u+NUARiVDRp6JLa90K3Aj8Xin1ELDT04n+F8BNAEopg1Lql8AY4DKl1Nc9mzcCFyql7lFKPYG7o/0/BykPESRiLNFkeSahXXNoHfd9/hg3r1lGU2dzj/UqWqsAaOwMzzMdX7hswqXevx/J/S12KbxEkChrqQDoNQF1OJuSMhGA00ct8M6VuubQOura6wMZlghyfR4yQmu9Glj9lWXLuv3tAh7y/Nd9nTLgUkTYO2PUwh5zpQHsrd3PvBGzvI+7CoYZQXY7eDAbHZfOgwvv4p7PHqGitZL82n0BmfRXiK/qKjRUck6AI/GfJZmnkxKVzLSUSVhMFqymSNodHfzys1+RFZ/JZWqp91Jjs62FKJNV7nQUMiK9GHzpsSN48vQHuG32jcwdPhOAv+35J1srtnvX6Sq6zDIwap8kW5P4xthzAChsKA5wNEK4L6u9WeAejjEpMiHA0fiP0WBk9rDp3kFfHz1tufdOxqLGEh7d/Fs+OPgxaw9v4M519/O7bc/hdPVtfloRfqToEj4RZbaSnZjFOZlneJe9kPcKm8u3Ad2LLjnz66vpntbBrilShAikI54J2c1GM5lxowMcTeBYjGbumX8H10/7gbe/1zuFH/L6vrcBONBQxAOfP8GG0k0UNx5tGEsxFEjRJXwqI24Ut8y8zvv4b3v+SYutFbvLAYDZIC1dfZXmmXqk3S4T7orAa7W77z4MpvkRAyXCZGFG2lRunX0DN834IRmx6ZgMJuYNd3etqGqr4RW9gt9v+zMtMnfjkCS/eMLnJiVPYFh0KpWt1QB8UbmT2vY6QFq6+sNqtjI8Oo3GzsZAhyIEbZ6iK8oUuncj+sKUlInezvYAp41aQFFjMbuq91BQX8Sydffx5On3E2WOCmCUwt+kpUv4xZ1zf8IZoxcB8Kp+k1XFnwBgNUUebzNxDCnWZNrs7dLaJQKuzd4GQJRFiofjGZ84lnMyz+D6aVd5lz2++X9xuVwBjEr4mxRdwi+sZiuL0+f3Wp6TlB2AaEJfomeKpYaOhgBHIoa6Npun6JKWrpMSY4nmJzOvB6CyrZodVbsDHJHwJym6hN+kx47g4UV3MzlFATB/xBzig3yetWCVaHVPgv3ApicpaigJcDRiKCtvdU9KkmgdOncuDpRKzuamGT8EYH3ZJmntGkKkT5fwq8TIBG6ece2JVxTHdeqIuaw7vJEmWzM7qnaTlZAZ6JDEEFXVWo3FaGFYVGqgQwkpk5MVE5NyyK/dx++2Pct1p1xOLImBDkv4mLR0CRGCUqKS+OX8nwGwumQt6zyD0Z7ojHlfXQHLP3uUu9Y/yLuFq466jpx1i76o62ggyZrQa+ofcXwGg4HvTVyK1RTJ/vpC7lz1KzaUbgp0WMLHpKVLiBAVGxHD6Nh0DjeX8ap+kw5HB29/spIFI+cyJi6D7MQskqyJrDz4MQBnjl7Eq/pfVLfXArDy4EesL/uc+SPmYDaaMRvMbCjbRLujHZPBxMiY4USaIjlv7NneqZ2E6M7msNFsayE9dmSgQwlJqVEpPLzobvJqNC/kvcw7RR+yaFTvvq8ifEjRJUQIu2ry5Tyc+xQA/yp4D4ANZblsILfXul13jM4fMYcZaVN5bteLNHU281HJf4762vvrCwHYXZMPwLVTr2T2sOmDnoMIXfUd7mFLhtJI9IPNarYyZ/gMNpRvRNcU0mxrIdYSE+iwhI9I0SVECEuPHcF5Y87mg+I1gPuSxbVTrqSosZjK1ipabG00dzYzKnYkzbYW0qJS+VbORVjNkfx45n+zp0bjwkVDRyNbK3cAEGG0cOvsG9hSsZ3dNfne8dWe3/0Sz+Pui3LTjB/yXtEqVh78mOunXcWMtCmB+icQAdRkc09kHx8RF+BIQl92Sha6ppBnd77Iz+bcFOhwhI9I0SVEiDt99CJsTju1HfXcuOD72JoMzBo27YTbTUzOYWK3CYqvcV1BbvkXqORsEiMTGBOfwbdyLsLutPPElqc53FwGwJ5azYr97/DJ4fUAPLfrRf5w9uO+SU4EtXbPwKhWswwXMVDfm34JG4q3cLCxBIfTMeRH9x+odnsHBxtLsDvtOFwOcsu/wOFycvXkywP6eZWiS4gQlxAZx9KcCwFItMZR1dTUr9cxGAzMHzmn13Kz0cztc25id3U+O6p2s7Vyh7fg6vJWwft8M/sb/XpfEfwcTgd76wrIjBvFhrJNxEfEszB9Hu0O9+C8VrMMcjxQESYL2YlZbKnYTkNno3fybHFi9R0NvFe4mj21mkhTBD+c8n1+v+05Wuy9p1oqa6lgXAD7qErRJYQ4oUhTBHOGz2Bq6iTvZUhwX4rsdNpYXbIWXbefS7MvZELS+ABGKgZbfUcDd294uNfyho4G1pe577aLNUf7O6ywlOYZdqOgvohTRkjRdSLt9nb+nv8a++oKvNNRATyy+beAeyqmrPhMmm0tpFiTmJyiGB49LFDhAlJ0CSH6INIUwWOnLWdX1R7iI+PISRzPU1v/wKHmMkqaSvndtme5MOvrfH3sWRgNJz8ijcPpoK6jgRRrkgw9EERe3/c2aw9vOOpz7xZ9OeTI8JjA/pCFi/kj5vDBwY95cc+rTE2ZSLRFitmjqW6r4W95r1LUWOxdppKyOXfMWXxw8GMcLgezh83gzNGLgu54IkWXEKJPYi0xLEif53387QmXkFu+lQ1l7jsm3y36kLyafH46+wYsxi8PMU6XE7vTjt3pILrbPH07q/J4dteL3sePLr6XOJmp4LhsDhudThsxJ/hRrm6r4a2C96lorSLSFMktM6874aVAu9NOTXsdueVfeAuuYdGp/HzOjzEbTdiddj45vIED9UWopGzSolPJiBs1aLkNZWnRKUxJmcjumnxWFa+VS/ZH0emwsXzjY4B7DtqEyDguyDrX2z+1ez/VYCRFlxBiQLITs8hOzOKKid9mX10Bv9v2HEWNJaw9tJ5zMs+gqq2GP+/6O2Ut5d5tlp/6c1KsyRgMBp7f/VKP1/vF+ge4YfrVTEud7NO4y1sq2FWdz5LM0/vUKhdIR5oqeSnvLTZXbPMumzt8JtdMuaLHetsqd/GX3f/otf1r+97iB5MvA9yXDVcVr6W2vZZzMs+ksbOJnVV5PV4b4MpJ32XByLnexxGmCC7I+tpgpiW6uW7qldy14SE2HtnMxePPC5nPpr+UNB32/r381J+H3A0HUnQJIQbNhKRsrpz4HV7a+zpvHXiftw68f9T17v/8iV7Lbpt9I7/54k8APLPzb3wr5yLOzjitzzG02zvYeGQzNW21qOTsYxZvv9/2HA2dTYyIGebzAm8wOJwO7vjwIWwOW4/lWyq2s6ViO9/OuZiRMcNx4epRcMVYolk29ycs3/go2yp3snjUfEqaSnl939vedXZV5/d4zRhzNOdnncOY+AzGxmf4NjHRg8VkYVbaND47ksvOqjxmnsSdyENJfo0G4HJ1acgVXCBFlxBikM0aNp23C1fS1NnsXXZB1tc4deRcDBj43+1/ocIzSTJAUmQiP5x6BeMSxnL/gjvZUrGddwo/ZMX+dzBi5MyMRSf93rur8/nTzr96H39yeD1XTvou80fMZmdVHntq95EQGU9+jaah032X57bKXSFRdBU3HfYWXDmJ47yD13Z5Y/+/e23z+Gn3eS9BXph1Lu8WreLXW//YY50pKROxGM0kRCYwKTmH0bHpJFllDsBAWjxqPp8dyeXD4jVSdHXjdDnZVP4FEaYI5gybGehw+kWKLiHEoLKaI3l08b1sKN1EbXsdU1In9bhF+575P2Nf3QGyE7OwOe0YDQYiTBGAe1qU88Yu4YvKnZQ2H+H1/W9T3lrJ5epSHE4Hmyu2UdFaxZKM04mN6D1q9/N5L3v//lrmmawuWctL+a/xUv5rx4x3U/lWDtQXMT1tCqePWkhlWxUJEfGkRCVhNVkD2hG3zd7G50e20tjZ5J1R4JYZ1zEpZQIdjk7MBhOFDQf54OAapqZOoqa9lk8OuYfzeHjR3T36fJ0xeiGFDcVEma1EWaIYFTOS00cvCEhe4vjGxGeQFpVCSVMp1W21pEYlBzqkoLC5fBt1HfUsSp/fo19oKJGiSwjhE8eaQ85gMKCSswGOeXng53N/zKqDa3j/4EesK91IdVsN+bX7vM+vKv6ExaNO5YZkd18ml8vF+wc/otPRCcAji+8hPiKOvbX7OOQZ1NVoMHLJ+PNJtiaxp0YzNXUSTpeTl/PfoLq9ljWH1rHm0LoecQyLTuWWGdeREqAfvf8c/ox3Cj/0Pk6PG+7tKBzpKVRzksaT022YjnMyz8BitPTqZB9tiebmmdf6IWoxGMYljKWqrYY39r/NDdOvCXQ4QeF1T2vugpHzTrBm8JKiSwgRdCxGMxeMO5eDTYfYU6N7FFxd1pd+zriC0cxPPoXDzWW8X7QagFtn/cg7Lc01U7/PjqrdzB8xh4TIeO+23eeQnJoykcPNR/i45FMONpYQZbZypKWChIh4KlureXbXi/xi3k990qHZ5XJxqKkUu8vO2PjMXu9R0ujuNPztnIuxmiI5Z/ICWurtx33NRJkHMSycN3YJm8q3sqs6n/qOhiG/X20OG232NoCQ7mcoRZcQImjdPONaHE4HR1oqsJgsDI9OA6CmrZZ7Nz7K3qoC4knkpfzXAViSeXqPVp/h0WmcO+as475HhCmCcQljGDftv3o99+zOF9lZncer+k0uV0sHtfCqaavj3o2PeB9/J+eSHv3XVhV/wo7qPKLMVs4YvRCjwUi0JYoW+jfjgAgtw6JTOX/sOaw8+BGv7F3BTTN+GOiQAqrr7ufTRy0IurG3+kKKLiFEUDMZTYyOS++xLNmaRFxELLml28kt3Q6A2WBi7vDB7Vx7uVpKdVsNG8pyMWDgcrV00A74f+3W/wzg9f1v89mRXJKtSXTYO9hXfwCAS8Z/Q4YNGKLOGL2QlQc/Yl9dAU2dzUN6/LqSplIAMuJGBziSgZFvshAi5BgMBs4efRrJUYlYTVZ+NO0qHjvtPjIH+YCcEBnHdVOvxGgwsr5sEzur8wb8mk9ueZqb1yyjqLGEuIhYfn36A9w841oyYtOpaKlkV/Ueb8EFsDj96H3jRPiLi4jl0uwLsDnt7K7ZG+hwAqquvR5wtwCGMmnpEkKEpHPHnsX3511MRWWDT1uChscM46rJl/PXvFc43HyEGWlTezzvcrlotrXwaelGbA4bC9NPOeYPg8PpoKixxPv462POxmq2MjlFMTlF4XK5aLW3YcCAEydRAb57UgReTuI4AD45tI4JieNJiRqaczI229xD0MRaet+1HEqkpUsIEdL8cemtqy/ZHs/AjN3tqt7DL9Y/wPtFq1ldspb7P3+cdwtXUd/R0GO9ho4m/tFt6Iobpl/NWRmLe6xjMBiIsUQTbYki1hITkoM/isGVETeKucNnUtp8hF/lPkWLrTXQIfldm73NO81YqF9i7XNLl1LqHGApUAm4tNb3H2Wd7wKPAD/VWr/bl22FECLYdN0NebCxhA8OrsFqjmRk9HB21+Sz8ciWXuuvPPgRq0vWctG4rzM6Nh2z0ewdbR/g2qlXhsSArCLwjAYjV0/+HoebyihvreRvef/kxhnXDJl+fiuLPubdoi+HTYkyWwMYzcD1qehSSkUDzwBTtNYdSqkVSqklWuuPu62TBVQBh/q6rRBCBKOEyHiWZl/ImwXv8k7hB72ejzRFsPzUO0mIjKO+o4G7NzyM3WnnXwXv9Vr3milXMCtNRhkXJ89gMHD7nJtYtu4+9tRq7lr/IHedcisVLVWMih151IGCQ53D6eCVvSv4vPzLk5pv51wc8sVmX1u6FgDFWusOz+MNwAWAt3DSWhcBRUqp5X3dVgghgtWSzNPJTsyioL6INwvcDfgZselcN+0HxEfEekfVT4xM4N75d1DUWEJVWw376go43FTG8Og0bpp5rbfVTIi+iLFEc0HW13ivaDXNthbu3vCw97lUazI3zriGETHDAxjh4NpWudNbcF0y/vwTDv0SKvpadA2DHoPENHqW+WzbpKRozGbf92tISxs6B8KhlCtIvuHOn/mmpU1mLpO5fM4FOJyOY/a5SkuLYyrjj/rcwGMYOvt3KOUKJ873qrSlXDHnIv5n9WMUN7iHUBgWk0JlSw2/3fYMS8YvZseRPbTa2vj+jEs5NWO2P8Lut2Pl22Hv5P/WvQXAg0vuQKX65rsUCH0tuiqB7v9K8Z5lPtu2rs73nQbT0uKoqhoaAw4OpVxB8g13km/4Gkq5Qt/yvWP2j3G4nJgMRlwuF8s3PkZdRz1v5X/Z9+mpz/7MmPgMFqfPp9NhY+6ImUF159+x8t1fV8hvtz0DuGeOSHYNC7nPwfGK574WXRuBMUqpSM9lwkXAH5VSyYBda93Y1237+P5CCCHEkGY0GL/s22SAX86/nX8XfojZYCLRmkBm3Gie3v4XihsPUdzo7l79ZsG7pEWlEB8Zz6KR8zAaTUxLmYTFZAlgJj11ODq9BVdO4jguGndegCMafH0qurTWrUqpG4HfK6WqgJ1a64+VUo8DtcCjSikDcDcwBrhMKWXTWn94rG0HOR8hhBBiSLGarXx3wiU9lj2y+Jc8tOkp79AlDpeD6rYaKlqr2FdXAMCl2RdwTuYZfo/3WLZW7ABgTFwGP5l1fch3mj+aPg8ZobVeDaz+yrJl3f52AQ95/jvhtkIIIYQYXFHmKB5ceBeFDcWMSxhDh6OTSFMEpc3lrCxazY7qPEqbjwQ6zB5q2moAdzEYjgUXyOCoQgghRFgyGoxkJ2ZhNBiJMlsxGoxkxKVz9ZQrAGjsCK6+UjaXHQCLKXwny5GiSwghhBhCIkwWosxWGjqP1w3b/+xOBwBmgxRdQgghhAgT8RHxQdfSZXe6W7rMRim6hBBCCBEmEiLiaLG3YvMUOsFAii4hhBBChJ34SPdYUsHU2tViawHAHMYTvUvRJYQQQgwxCZHxADR0NpzU+g6ngw5Hpy9DorKtGpPBRJwl1qfvE0jh24YnhBBCiKMaEe2eha+0uZxxCWN7Pe9yuXj7wEo+O5KL1RRJU2czAPecegfJ1iSfxNRiayUtOvWY02uFA2npEkIIIYaYzLjRALyq3+Qf+a/1ev5fB95jdclaWmytNHU20+m00em0oWsLfBKP0+Wk1dZGtDnKJ68fLKSlSwghhBhiRsYMJzEygfqOBj4/soUdVbtJsSYTYYqgsrWKZk//qrMzTuNbORdxqKmURzf/jsKGYhakzxv0eNrtHbhwEWORoksIIYQQYcRkNLH81GVsKNvEZ2W5dDg6qGqr7tFva3rqFC4Zfz4A6TEjiDBFUNhY7JN4Wu1tAESbo33y+sFCii4hhBBiCIowWTgrYzFnZSz2LnO6nBTUF1HTVsv8kXO80/GYjCbGxmeyr66AooZishLGDGosrbZWAGIs4V10SZ8uIYQQQgDuqYMmJI1nQfq8XvMfTkzKBuDJrX9gY9nmQX3fFru76Ar3Pl1SdAkhhBDihM7KOI3zxpwNwEt7X2dd6eeDNoxEV0tXtLR0CSGEEGKoizBZuGj8edww/WqMBiOv6je5/T+/ZGvFDmxOO2XN5XT2swjr6tMVE+YtXdKnSwghhBAnbVrqZG6ffRPvFH6ArivghbyXvc/FWmK4dur3meC5FHmyWmyejvTS0iWEEEII8aWsXTJFIwAACyBJREFUhExumXkd541dwoSkbMbGZxIXEUuzrYV/7n2zz6/35eVFaekSQgghhOjBaDBy0biv91h2+39+SX3Hl1ML1bTV0mJvZWTMCCzHmMja6XLy8aFPARkyQgghhBDipIyNz0TXFXDzmmXEWKJp8bRgAdw4/RrMRjPRliiMMSMoaijl45JP2Va1CwCzwUSKj6YYChZSdAkhhBBiUMwdPot9dQdw4cLutGMymHC4HAD8aedfj7md1RTJ8gXLwnreRZCiSwghhBCDZGH6POYOnwm4iDBFAO5LjB+VfEpRw0FSo1M5UF9EvDWWaFMMc4fPYHrqFOxOO/ERcYEN3g+k6BJCCCHEoIkwWXo8TolK5jL1zR7L0tLiqKpq8mdYQUHuXhRCCCGE8AMpuoQQQggh/ECKLiGEEEIIP5CiSwghhBDCDwwulyvQMQghhBBChD1p6RJCCCGE8AMpuoQQQggh/ECKLiGEEEIIP5CiSwghhBDCD6ToEkIIIYTwAym6hBBCCCH8QIouIYQQQgg/kKJLCCGE8DOllPz+DkFDYqcrpdIDHYPwnaG2f5VSWUqpWKWUIdCx+JpSaly3v4dCvpOVUlmBjsNflNtSpZQl0LH4g1JqulLqDaVUvNbaGeh4fM1zrIoZCt/dk2UOdAC+pJSKAS4BzlJKFQP7tNavKaUMWuuwG4pfKWUFfgds0Fr/XSllDOcvtlIqGrgQ+I5Saj/wkdZ6TYDD8hmlVCxwHvA9oALYArwQ0KB8SCl1PvCCUuoerfVfcJ8kOgIclk8opRKBu4FFwC1AUbgepwCUUlHA+cBiYCOQAFQHNCgf8uzfXwBTARdwCvBRQIPyIc+x6kLgUqAOyCWMj1V9Ee4tXRcDKcDPgDzg50qpmVprV5hW3hlAHPBbpZQpzAuuqcAfgMPAT4FoYFa4NtkrpcYCTwGtwH8DJcBoz3Nh9Vnutg+dwBrgKqVUgtbaEY77VymVA7wC7AVOA4qVUhGAyfN8WO1fj1MAm9b6dmAXYPecNIZdvkqpRcAmoBy4DFgBFHqeC6tcwfv9vQV3IX09sAdIDsdc+yPsDmDg/iB7dvxZQJ7WuhF4B1gFPAEQTmeQ3X6I0rTWVwD7cf9Ah3O/gYNAE5CrtS7D/YOVGcaFZjlQr7V+X2tdC8QCzUqplDD7LBu67cNs4E3cP8p3KKWScRfX4eYQsBV3i881wI+B3wB3QPgdqzw/vmcCNqXUUuC7wHLcJ1Fhla/HduAZrfVvtdYtwEjgmxCWuYL7ZOE84JDWugGIANKA6QGNKkiEzQ+y59rx9Z5r5S7Pgfsw8CiA1toO/BEwKaVODWSsg+Er+Xb9SO32/P+HwC1KqXFaa2c4nGF0zxdAa90M3OvZrwAFwKeedYcHKMxBc5R824FHPM/NAJKASOAtpdS3PMtDcj9/9burlDJ5nqrGfaK0HvgvYJlnv4dsrnDMfbsG9/f2P1rr+4D3gHOVUmcGLNBB8tVjlafQKMPdFaJMa/0A7mPzKUqpSwMa7CA4yv5twZ1fl09wnzSG9Oe4y1HytQFvA5crpf4FxOM+sXhVKXWFZ5uQz7u/wqLo8jTFLwOW4u7D1eVxYIRS6juex3XA50CjfyMcXMfKV2vd6LmsmIf7csVvPE+l+j/KwXOcfOu7rXYK8JHnUs0t/o1wcB0n3zrPn/la65u11o8A7wLpnudD7qz5aLlqrbv6bZ0DXAlk4S5KxiqlpnnWCblc4bj79hPgOa31fs+itcA2wP7V1wglxzk2/w1IBhYAePJ+mRD/TTrO/u3otlo2MMuzPCQ/x12Ok+9vgP/Bfay6V2v9NPAn3C1eIZ/3QIT0B7ybCGAn7o6JC7ru/vGcQd4FPKaUmgDMxv0DFdJFF8fI13P20PVhvgq4SCn1ChDqLT/Hy7frEmoG7i/9H4HGEL+seqx8u3LK8DxeBMzBfSIRqo6VayTuoqMUeMzz/FnA2eG4bwG01vs8fRUBpgGZuFuEQtmxjs024OfA7UqpkZ7P8mTc/X9C2XGPVR7/BmI8nc1D3TE/z8AM4DYApdRi3CfGn/k9wiBjcLlCr+BUSk3EXVl/BOzRWjd7LknMAG70LPtNt/Wvw11hm4HnPX2AQkY/8rXiPpO6Enhaa50fgLD7rS/5eg5mi3Bfjvkb8KzWOqQO3P3Yv8uAmcBqYLXW+nAAwu6XPu7bOK11k+dOtyW4z5oPBCr2/ujHvn0QGIf7Uvl7obRvoV/53oa7f6IDeFFrXRqAsPutr/l6tpkPjAFWdGvVDQn92L+v4r75Zz/w91Dbv74QMkWXp4OtSyl1I+5rxEXAGUCM1vrqbuv9BPdtub8JtWKju4Hk62kJiO7q/xIKBpjvRGCa1vp1/0fePwPMdzgwVmu9yf+R991Av7sqxIZOGOC+TQMytNZf+D/y/hmE/WsKpeJDfov69Hm2AvFa60r/Rx6cQqKZ3tOaEeV5mAh8oLV+DbgX+JZS6txuq78OdAK/Vkrd7LnmHFIGmq+ns2pIFVz0P1+r1npvqBVc9D/fSK11RSgVXPQ91ye7f3dDreBiYPu2KtQKLgZ4bA61ggv5LerL57ldCq6egr7oUkotwD38wZNKqSm4K+wxAFrrGty3Gj/ZbRMT7j5M23A3Z3b6N+KBkXz7nG+7fyMemEHIt4MQMYBctzM0P8shs29BjlVIvmH1efaXoL286OlkeB/ua8GvAX/BvTPrgVu01hM960UC/wLu1FrvUkqlAtYQ7Ash+Uq+YZHvUMoVJF8kX8k3hPP1t2Bu6XLhvnPpI+2+Vf5+4BTtvvXUrpS61bNeCu6BMfcAaK2rQ3SnS76SL4RHvkMpV5B8JV83yTc08/WrYJ57sRV4Q2t9qNuyjZ7/3wOcp5R6AvfwD1tDqV/AMUi+km+45DuUcgXJFyRfyVeclKAtujydZ7vv9DFA1x0gEcCvcA8DUeS5vhzSJF/JlzDJdyjlCpIvkq/kK05a0BZdRzESqFVK/RPoAFZprYsDHJMvSb6Sb7gYSrmC5Cv5hpehlq9PBW1H+u6UUiNwj2S7C3hNa/1ygEPyKclX8g0XQylXkHwl3/Ay1PL1h1Bp6XICzwNPDpHbUCXf8DaU8h1KuYLkG+4kXzEgIdHSJYQQQggR6oJ5yAghhBBCiLAhRZcQQgghhB9I0SWEEEII4QdSdAkhhBBC+IEUXUIIIYQQfhAqQ0YIIcQxKaUWAQ8Bk3FPwpsExAJ/1Vq/cYJtrwbO1Fpf7eMwhRBDnLR0CSFCntZ6A/Ai7qlJbtBaXwZcB9yjlLotsNEJIYSbtHQJIcKS1vqIUmoZsEIp9SbwByAPSAG2aK2fUUrlAFcCo5RSTwPvaK0/VEr9BJgAtAGJwG1a6+bAZCKECBdSdAkhwtlmIAYYBvxaa/0JgFJqp1Lq31rr/Uqpl3BfXrzF89wS4GKt9Tmexw8By4B7A5KBECJsSNElhBgKjMCZSqnvAa1AMjAeKDvKuucDqUqpZzyPU4EjfolSCBHWpOgSQoSzeUALcDYwS2t9MYBSaiZgOsY2BmCj1vpGz7oGINoPsQohwpx0pBdChCWl1AjgcWA57n5ctZ7lRmB0t1XbAZNSyqCUugpYCZyllOo6Kf0mcKvfAhdChC2Z8FoIEfKUUguAB4GpwBu4h4xIAP6htf4/pVQm8CqwH6gBLgF2AtcCUcAKoABYo7V+QSl1K7AQOARYgZ9prdv9m5UQItxI0SWEEEII4QdyeVEIIYQQwg+k6BJCCCGE8AMpuoQQQggh/ECKLiGEEEIIP5CiSwghhBDCD6ToEkIIIYTwAym6hBBCCCH84P8B138LBCviMhcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['.SPX', 'vola']].plot(subplots=True, figsize=(10, 6));  \n",
    "# plt.savefig('../../images/ch01/spx_volatility.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Idioms & Paradigms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "uuid": "beca497d-4c20-4240-b003-4c79c26154be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.26 s ± 24.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "loops = 2500000\n",
    "a = range(1, loops)\n",
    "def f(x):\n",
    "    return 3 * math.log(x) + math.cos(x) ** 2\n",
    "%timeit r = [f(x) for x in a]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "uuid": "931fd1fc-cc54-4045-802f-acb8f094a23f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77.3 ms ± 1.17 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a = np.arange(1, loops)\n",
    "%timeit r = 3 * np.log(a) + np.cos(a) ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "uuid": "8d1602b3-a490-4d1a-97d9-112825d86185"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36.4 ms ± 233 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "import numexpr as ne\n",
    "ne.set_num_threads(1)\n",
    "f = '3 * log(a) + cos(a) ** 2'\n",
    "%timeit r = ne.evaluate(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "uuid": "6994f16a-1802-4abe-851d-a48e0ead154d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.9 ms ± 443 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "ne.set_num_threads(4)\n",
    "%timeit r = ne.evaluate(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data-Driven & AI-First Finance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data-Driven Finance "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This sub-section requires a Eikon license. Therefore no code is provided.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AI-First Finance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('../../source/tr_eikon_eod_data.csv',\n",
    "                   index_col=0, parse_dates=True)\n",
    "data = pd.DataFrame(data['AAPL.O'])  \n",
    "data['Returns'] = np.log(data / data.shift())  \n",
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "lags = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = []\n",
    "for lag in range(1, lags + 1):\n",
    "    col = 'lag_{}'.format(lag)\n",
    "    data[col] = np.sign(data['Returns'].shift(lag))  \n",
    "    cols.append(col)\n",
    "data.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC(gamma='scale')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data[cols], np.sign(data['Returns']))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Prediction'] = model.predict(data[cols])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Strategy'] = data['Prediction'] * data['Returns']  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['Returns', 'Strategy']].cumsum().apply(np.exp).plot(figsize=(10, 6));  \n",
    "# plt.savefig('../../images/ch01/AAPL_trading_strategy.png');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:training@tpq.io\">training@tpq.io</a>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
